{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from pandas.core.frame import DataFrame\n",
    "import pandas as pd  \n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>07:40:54.493</td>\n",
       "      <td>0.953239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>07:40:54.527</td>\n",
       "      <td>0.941747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>07:40:54.564</td>\n",
       "      <td>0.945992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>07:40:54.564</td>\n",
       "      <td>0.944917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>07:40:54.598</td>\n",
       "      <td>0.954352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            time       acc\n",
       "0  07:40:54.493   0.953239\n",
       "1  07:40:54.527   0.941747\n",
       "2  07:40:54.564   0.945992\n",
       "3  07:40:54.564   0.944917\n",
       "4  07:40:54.598   0.954352"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"AccData_Repository.csv\",names = [\"time\",\"acc\"])\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the acc dist mean:-52.95mg and std:5.89mg\n"
     ]
    }
   ],
   "source": [
    "acc_list = np.array(dataset[\"acc\"])\n",
    "acc_time = np.array(dataset[\"time\"])\n",
    "acc_list = (acc_list - 1.0)*1000\n",
    "acc_mean = np.mean(acc_list)\n",
    "acc_std  = np.std(acc_list)\n",
    "acc_mean_str = \"{:.2f}\".format(acc_mean)\n",
    "acc_std_str = \"{:.2f}\".format(acc_std)\n",
    "print(\"the acc dist mean:%.2fmg and std:%.2fmg\" %(acc_mean,acc_std))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc_min = -128\n",
    "acc_max =  128\n",
    "CONST_AXVLINE_COLOR = \"#1f1f1f\"\n",
    "plt.figure(figsize=(16,9),facecolor='#0f0f0f',edgecolor='black')\n",
    "plt.subplot(facecolor='k')\n",
    "ax = plt.gca()#用于获取当前坐标轴的信息\n",
    "ax.yaxis.label.set_color('white')\n",
    "ax.xaxis.label.set_color('white')\n",
    "#ax.spines['left'].set_color('white') \n",
    "ax.set_ylim(acc_min, acc_max)\n",
    "plt.yticks([-128,-96,-64,-32,0,32,64,96,128])\n",
    "#ax.grid(axis=\"y\",color=\"#4f4f4f\")\n",
    "ax.axhline(y=0,color=CONST_AXVLINE_COLOR,linewidth=3)\n",
    "ax.axhline(y=32,color=CONST_AXVLINE_COLOR,linewidth=1)\n",
    "ax.axhline(y=-32,color=CONST_AXVLINE_COLOR,linewidth=1)\n",
    "ax.axhline(y=64,color=CONST_AXVLINE_COLOR,linewidth=1)\n",
    "ax.axhline(y=-64,color=CONST_AXVLINE_COLOR,linewidth=1)\n",
    "ax.axhline(y=96,color=CONST_AXVLINE_COLOR,linewidth=1)\n",
    "ax.axhline(y=-96,color=CONST_AXVLINE_COLOR,linewidth=1)\n",
    "acc_slice = acc_list[:1024]\n",
    "acc_frame = []\n",
    "for k in range(len(acc_slice)):\n",
    "    acc_frame.append(int(k/32))\n",
    "df = pd.DataFrame()\n",
    "df[\"frame\"]= acc_frame\n",
    "df[\"acc\"] = acc_slice\n",
    "sns.violinplot(y=acc_slice,x=acc_frame,split=True,color=\"white\",inner=None)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
